ikostrikov/pytorch-a2c-ppo-acktr-gail
Pytorch A2c Ppo Acktr Gail
This is a PyTorch reinforcement learning library designed for training agents in simulation environments. It provides a collection of deep reinforcement learning algorithms focusing on policy gradient methods and trust-region optimization.
The library implements a suite of policy gradient algorithms, including A2C and PPO, alongside a framework for imitation learning using Generative Adversarial Imitation Learning. It specifically features a scalable implementation of the ACKTR algorithm, utilizing Kronecker-factored approximations to enable efficient trust-region optimization.
The codebase covers broader capabilities including standardized environment interfaces for simulation integration, experience-based batch processing, and tools for visualizing agent behavior and training progress.
Features
- Reinforcement Learning Training - Provides a comprehensive framework for training deep reinforcement learning agents in simulation environments using PyTorch.
- Actor-Critic Architectures - Utilizes actor-critic architectures with separate networks for action probabilities and state value estimation.
- Kronecker-Factored Trust Region Methods - Implements Kronecker-factored approximations to scale trust-region optimization for large neural networks with lower compute costs.
- Policy Gradient Methods - Ships a suite of policy gradient algorithms, including A2C and PPO, for optimizing agent behavior.
- Proximal Policy Optimization - Provides advanced policy optimization through methods such as Proximal Policy Optimization and trust-region updates.
- PyTorch Reinforcement Learning Libraries - Offers a collection of standard deep reinforcement learning algorithms implemented in PyTorch for simulation environments.
- Trust Region Policy Optimization - Supports scaled trust-region policy optimization using Kronecker-factored approximations for stable and efficient updates.
- Simulation Training Environments - Integrates deep learning models with physics engines and game simulators for behavioral training and testing.
- Adversarial Imitation - Enables agents to mimic expert behavior using generative adversarial imitation learning frameworks.
- Clipped Policy Objectives - Implements clipped surrogate objectives within PPO to maintain training stability and prevent catastrophic performance drops.
- Environment Wrappers - Offers a standardized API wrapper to decouple reinforcement learning agent logic from various simulation engine physics.
- Simulation Environment Interfacing - Implements a standardized interface for connecting agents to simulated environments for action-reward based learning.
